Introducing the 'ACI': Artificial Customer Intelligence.

Imagine what you could do if you could read your customers’ minds.

Imagine being able to identify what was drawing them toward your product—appealing discounts, promotions, or, if yours is a seasonal product, the weather. Better still, imagine knowing what was putting them off—things like poor pricing or more attractive competition.

Now what if someone used this information to build an infallible AI model of customer choice that you could use to shape your business strategy?

This isn’t imaginary; nor is it the future. It’s happening now.

In these newly chartered waters of #AI-assisted business, Evo’s solution is pioneering: driven by #data, powered by #machinelearning, and steered by human managers whose #business expertise helps chart the course. Tweet This

AI and business haven’t had an easy alliance... until now.

Historically, the biggest problem with using AI in business has been time.

Not only has it taken time to harvest the huge amount of data needed, but there’s been significant delay between the training phase (in which the AI learns a new capability from the presented data) and the inference phase (in which this capability is applied to new data).

In practical terms this has been problematic—with managers gleaning actionable insights from old, redundant data.

Another obstacle in forging the human machine alliance in business has been setting limitations.

Ask a customer how much they’d ideally like to pay for a product and they’ll tell you “nothing”. Unless we train an 'AI customer model', so we can explicitly know this is out of the question, why should it come up with anything different?

Customer Choice Modelling has had its problems. But where others have seen problems, Evo’s team has searched for solutions.

From both business and technical perspectives, Evo’s Artificial Customer Intelligence model is completely unique.

Seeing the shortcomings of having two previously separate phases (training and inference), we’ve merged the two to create a model run on live feedback. This means that rather than wasting time training a new model, and wasting yet more time running new inferences, we can do both simultaneously with incrementally more accurate results.

And as our Artificial Customer Choice model can pick out even weak-signalled patterns (like a product’s average price change) among the noisiest of data sets (volatile sales data, for example), as an ACI business solution our self-learning model is faster, more adaptive, and more evolutionary (hence why we’re called Evo) than any other AI solution to date.

We’re not just technically innovative. We recognize that the best model for artificial intelligence is our own human intelligence. That’s why our model puts business executives in the driving seat, letting them input their organization’s rules, constraints, and objectives.

It’s the best example so far of the human-machine alliance in business, using human decision-making to guide machine-learning’s quantitative legwork. Almost 70% more effective than our previous approach to demand forecasting (known as the Demand Learning Model, or DLM, v2.0).

The advantages of such an approach are clear. The ability to leverage such deep reserves of data and shape your business strategy to meet the demands of an ACI model puts you in the same shoes as companies like Amazon, whose enormous value comes from its data-driven, customer-centric approach.

The key difference is that with Evo’s Artificial Customer Intelligence model helping you optimize your strategy, you don’t need the amount of data or investment of a retail juggernaut to reap the rewards.